“Our model takes into account the complexity of buildings and distances between classrooms, which were not taken into account in previous timetable programs,” said Ye, who also serves as director of the Texas A&M Center for Geospatial Sciences, Applications and Technology and founding director of the Urban AI Lab at the Texas A&M Institute of Data Science.

Applications

By creating a digital twin of Texas A&M University, researchers can estimate usage at a given location at a given time. This has a variety of applications, including maximizing class sizes, reducing commute times through more efficient campus transportation, and even providing first responders with estimates of how many people will be in a given location in the event of an emergency.

“The ability to replicate student mobility using a digital twin will positively impact multiple aspects of campus life, from shorter commutes to full class schedules, and provide students with the best value for their tuition money,” Ye said.

The researchers say that using a digital twin to optimize class scheduling not only leads to smoother campus operations, but can also encourage students to take interdisciplinary courses. Because of the nature of class scheduling, students may shy away from taking an elective outside of their major if the timing or location clashes with their core courses. The ability to maximize interdisciplinary courses can provide students with a more well-rounded education. While this method has not yet been used for the fall 2024 class schedule, there is hope that it will be used in future semesters.

Other Texas A&M faculty collaborating on the project include Dr. Galen Newman, professor and chair of the Department of Landscape Architecture and Urban Planning, Dr. Patrick Suermann, professor and interim dean of the School of Architecture, and Dr. Shannon Van Zandt, professor in the Department of Landscape Architecture and Urban Planning and coordinator of the Ph.D. program in Urban and Regional Studies.